feat(identity): local-feature matcher plus text lane as the default backend

Replaces the DINO gallery as the recognition path with the measured design
from docs/research/2026-09-28-recognition-architecture/DECISION.md: RootSIFT
over a per-SKU reference pyramid, similarity-constrained RANSAC and a hue
check, fused with an OCR text lane that vetoes a match whose package text
names a different catalog row and answers on its own when geometry finds
nothing.

- identity_lanes.py: LocalLane, TextLane, DecisionPolicy. The text lane
  builds each row's signature from the catalog name plus its packshot OCR, so
  a row with no enrolled image is still answerable.
- engine.py: every backend groups scores by canonical SKU, so no
  view-suffixed id reaches the wire and a name and price always resolve.
  decide_crop fuses the lanes; score_crop_lanes feeds the live path. Both
  lanes are built in warm_up (non-fatally) so the first frame is not the slow
  one, and the text lane shares the engine's OCR lock.
- live.py: decides per crop on the lane's own rank order. The lane's order
  comes from its rank key and its value is only the gate, so re-sorting by
  value named a different SKU than the lane chose; averaging in an empty
  observation also diluted a good confidence below the accept line.
- identity.py: a `local` score scale whose gate is one absolute line, because
  the value is already a margin over the runner-up.
- app.py, run-demo-server.sh: `--identity local` is the default; the demo
  server no longer starts on the leaked `stock-lopia` catalog.
- eval_live.py: the replay vocabulary accepts `local`, so a recording from
  the shipped server can be scored.
- README.md: documents the three backends and what each is for.

Measured on the corrected image6 labels at live resolution: 36/61 in-catalog
facings named at 95% precision, 0 of 48 unknowns accepted, 11 labels sourced
from the text lane alone. Through the real server: /v1/recognize 90%
precision, /v1/stream 100% precision, no view-suffixed ids on either.

recognization/docs/research/2026-09-28-recognition-architecture/evidence/README.md

@@ -0,0 +1,53 @@
+# Evidence scripts (2026-09-28 audit)
+
+Read-only against product code: every script imports from `code/` and writes
+only into this folder. Run from `recognization/` with `.venv/bin/python`.
+
+`image6_v2.json` is the corrected image6 label set (26 of 111 v1 labels
+changed; every row carries `v1_label`, `source`, and a `note` where changed).
+`image6-v1-labels-overlay.jpg` shows the v1 labels on the shelf; green = catalog
+label, red = unknown. `sheet.py` regenerates per-SKU contact sheets.
+
+| Step | Command (prefix `E=docs/research/2026-09-28-recognition-architecture/evidence`) | Produces |
+|---|---|---|
+| Original matcher, both catalogs, full/live/live-with-unscaled-boxes | `.venv/bin/python $E/diag.py --stock stock-lopia-clean --labels $E/image6_v2.json [--live] [--bug] --out $E/run_stock-lopia-clean_<full\|live\|livebug>.pkl` (repeat with `--stock stock-lopia`) | `run_*.pkl` |
+| Score them under v1 or v2 labels | `.venv/bin/python $E/analyze.py <labels.json> $E/run_*.pkl` | identity top-1, argmax, current gate |
+| Matcher variants | `.venv/bin/python $E/variants.py --stock stock-lopia-clean --labels $E/image6_v2.json [--live] --scales 200,400 --root --geom affine --out $E/v_ms_root_aff_<full\|live>.pkl` (also `--tmpl-h 250/400`, no flags = raw) | `v_*.pkl` |
+| Variant ranking + open-set frontier | `.venv/bin/python $E/an2.py $E $(basename -a $E/v_*.pkl)` (`LABELS=image6_v2_lfo.json` for the leave-facing-out runs) | correct at 100/95/90 % precision |
+| Leave-facing-out catalog | `.venv/bin/python $E/lfo_setup.py`, then `variants.py --stock $E/cat_<pack\|full> --labels $E/image6_v2_lfo.json --scales 200,400 --root --geom affine [--live] --out $E/lfo_cat_<pack\|full>_<full\|live>.pkl` | `lfo_*.pkl` |
+| Unrelated-shelf distractors | `YOLO_OFFLINE=1 .venv/bin/python $E/distract.py stock-lopia-clean $E/cat_full` | `distract.pkl` |
+| Catalog growth, exhaustive vs descriptor index | `YOLO_OFFLINE=1 .venv/bin/python $E/scale.py` | stdout |
+| DINO retrieval under growth | `YOLO_OFFLINE=1 .venv/bin/python $E/dino_scale.py` | stdout |
+| OCR read-rate | `.venv/bin/python $E/ocr_exp.py` | `ocr.pkl` |
+| OCR signatures, then fusion | `.venv/bin/python $E/ocr_fuse.py && .venv/bin/python $E/fuse_eval.py` | `sig.pkl`, stdout |
+| Per-SKU contact sheets + the labelled shelf | `.venv/bin/python $E/sheet.py <outdir>` | `gt_<label>.jpg`, `shelf_labels.jpg` |
+
+Timings in DECISION.md come from `scale.py` (single process). The other
+scripts ran up to ten processes at once, so their ms figures are relative only.
+
+## Verified 2026-09-28
+
+Every command above was run from `recognization/` and produced its listed
+output. Two rows were wrong as written:
+
+- The table said `python`; the `python` on this machine's PATH has no `cv2`, so
+  every row failed on import. It is `.venv/bin/python`.
+- `an2.py` takes the folder and then file names *inside* it, so `v_*.pkl`
+  expanded in the working directory, where those files do not live. It is
+  `$(basename -a $E/v_*.pkl)`.
+
+`sheet.py` was missing from the table. It takes its output directory as a
+positional argument, not a flag: `sheet.py --help` reads `--help` as a
+directory name, writes nothing, and still exits 0. `analyze.py` and `an2.py`
+with no arguments raise `IndexError`; neither validates its own usage.
+
+v1 labels are no longer a file on disk — `eval/labels/image6.json` is the v2
+set. To score a run under v1, rebuild it from the `v1_label` field:
+
+    .venv/bin/python -c "import json;d=json.load(open('$E/image6_v2.json'));\
+    [f.update(label=f.pop('v1_label')) for f in d['facings']];\
+    json.dump(d,open('/tmp/image6_v1.json','w'),ensure_ascii=False)"
+
+That reconstruction reproduces DECISION.md's v1 numbers exactly: `analyze.py`
+gives ungated top-1 26/35 at both resolutions and the current gate 24/35 full,
+23/35 live (§2 row 15, F-01).